Systems and methods for tracking a model

ABSTRACT

An image such as a depth image of a scene may be received, observed, or captured by a device. A grid of voxels may then be generated based on the depth image such that the depth image may be downsampled. A model may be adjusted based on a location or position of one or more extremities estimated or determined for a human target in the grid of voxels. The model may also be adjusted based on a default location or position of the model in a default pose such as a T-pose, a DaVinci pose, and/or a natural pose.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No. 13/889,901 filed May 8, 2013, which is a continuation of U.S. patent application Ser. No. 13/289,823 filed Nov. 4, 2011, which is a continuation of U.S. patent application Ser. No. 13/156,457, filed on Jun. 9, 2011, now U.S. Pat. No. 8,325,984 issued Dec. 4, 2012, which is a continuation of U.S. patent application Ser. No. 12/621,013, filed on Nov. 18, 2009, now U.S. Pat. No. 7,961,910 issued Jun. 14, 2011, which is a continuation-in-part of U.S. patent application Ser. No. 12/575,388, filed on Oct. 7, 2009, the disclosure of each of which is incorporated herein by reference in its entirety.

BACKGROUND

Many computing applications such as computer games, multimedia applications, or the like use controls to allow users to manipulate game characters or other aspects of an application. Typically such controls are input using, for example, controllers, remotes, keyboards, mice, or the like. Unfortunately, such controls can be difficult to learn, thus creating a barrier between a user and such games and applications. Furthermore, such controls may be different from actual game actions or other application actions for which the controls are used. For example, a game control that causes a game character to swing a baseball bat may not correspond to an actual motion of swinging the baseball bat.

SUMMARY

Disclosed herein are systems and methods for tracking a user in a scene. For example, an image such as depth image of a scene may be received or observed. A grid of voxels may then be generated based on the depth image such that the depth image may be downsampled. For example, the depth image may include a plurality of pixels that may be divided into portions or blocks. A voxel may then be generated for each portion or block such that the received depth image may be downsampled into the grid of voxels.

According to one embodiment, a background included in the grid of voxels may then be removed to isolate one or more voxels associated with a foreground object such as a human target. A location or position of one or more extremities such as a centroid or center, head, shoulders, hips, arms, hands, elbows, legs, feet, knees, or the like of the isolated human target may be determined or estimated. Additionally, dimensions such as measurements including widths, lengths, or the like of the extremities may be determined or estimated.

A model may then be tracked or adjusted based on the location or position of the one or more extremities and/or the dimensions determined for the human target. For example, the model may be a skeletal model that may include body parts such as joints and/or bones. In one embodiment, when a location or position may have been estimated for one or more of the extremities of the human target, one or more of the body parts such as the joints and/or bones of the model may be adjusted to the estimated location or position of the one or more extremities associated therewith. According to another embodiment, when a location or position may not have been estimated for one or more of the extremities of the human target, one or more body parts such as the joints and/or bones of the model may be relaxed based on default locations or positions in a default pose such as a T-pose, a DaVinci pose, a natural pose or the like. For example, a body part such as a joint of the model may be relaxed by adjusting the joint to a default location or position associated with the joint in the default pose such that the model may return to a neutral pose. The joints of the model may then be magnetized or adjusted to a location or position of, for example, a voxel in the human target that may be closest to the default location or position.

The model may then be processed. For example, in one embodiment, the model may be mapped to an avatar or game character such that the avatar or game character may be animated to mimic the user and/or the adjusted model may be provided to a gestures library in a computing environment that may be used to determine controls to perform within an application based on positions of various body parts in the model.

This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used to limit the scope of the claimed subject matter. Furthermore, the claimed subject matter is not limited to implementations that solve any or all disadvantages noted in any part of this disclosure.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A and 1B illustrate an example embodiment of a target recognition, analysis, and tracking system with a user playing a game.

FIG. 2 illustrates an example embodiment of a capture device that may be used in a target recognition, analysis, and tracking system.

FIG. 3 illustrates an example embodiment of a computing environment that may be used to interpret one or more gestures in a target recognition, analysis, and tracking system and/or animate an avatar or on-screen character displayed by a target recognition, analysis, and tracking system.

FIG. 4 illustrates another example embodiment of a computing environment that may be used to interpret one or more gestures in a target recognition, analysis, and tracking system and/or animate an avatar or on-screen character displayed by a target recognition, analysis, and tracking system.

FIG. 5 depicts a flow diagram of an example method for tracking a user in a scene.

FIG. 6 illustrates an example embodiment of a depth image that may be captured or observed.

FIGS. 7A-7B illustrate an example embodiment of a portion of the depth image being downsampled.

FIG. 8 illustrates an example embodiment of hands and feet that may be calculated based on arm and leg average positions and/or anchor points.

FIG. 9 illustrates an example embodiment a model that may be generated.

FIG. 10 depicts a flow diagram of an example method for tracking a model associated with a user in a scene.

DETAILED DESCRIPTION OF ILLUSTRATIVE EMBODIMENTS

FIGS. 1A and 1B illustrate an example embodiment of a configuration of a target recognition, analysis, and tracking system 10 with a user 18 playing a boxing game. In an example embodiment, the target recognition, analysis, and tracking system 10 may be used to recognize, analyze, and/or track a human target such as the user 18.

As shown in FIG. 1A, the target recognition, analysis, and tracking system 10 may include a computing environment 12. The computing environment 12 may be a computer, a gaming system or console, or the like. According to an example embodiment, the computing environment 12 may include hardware components and/or software components such that the computing environment 12 may be used to execute applications such as gaming applications, non-gaming applications, or the like. In one embodiment, the computing environment 12 may include a processor such as a standardized processor, a specialized processor, a microprocessor, or the like that may execute instructions including, for example, instructions for receiving a depth image; generating a grid of voxels based on the depth image; determining whether a location or position has been estimated for an extremity of a human target included the grid of voxels; adjusting a body part of a model associated with the extremity to the location or position when, based on the determination, the location or position has been estimated for the extremity; and adjusting the body part of the model to a closest voxel associated with the human target when, based on the determination, the location or position has not been estimated for the extremity, or any other suitable instruction, which will be described in more detail below.

As shown in FIG. 1A, the target recognition, analysis, and tracking system 10 may further include a capture device 20. The capture device 20 may be, for example, a camera that may be used to visually monitor one or more users, such as the user 18, such that gestures and/or movements performed by the one or more users may be captured, analyzed, and tracked to perform one or more controls or actions within an application and/or animate an avatar or on-screen character, as will be described in more detail below.

According to one embodiment, the target recognition, analysis, and tracking system 10 may be connected to an audiovisual device 16 such as a television, a monitor, a high-definition television (HDTV), or the like that may provide game or application visuals and/or audio to a user such as the user 18. For example, the computing environment 12 may include a video adapter such as a graphics card and/or an audio adapter such as a sound card that may provide audiovisual signals associated with the game application, non-game application, or the like. The audiovisual device 16 may receive the audiovisual signals from the computing environment 12 and may then output the game or application visuals and/or audio associated with the audiovisual signals to the user 18. According to one embodiment, the audiovisual device 16 may be connected to the computing environment 12 via, for example, an S-Video cable, a coaxial cable, an HDMI cable, a DVI cable, a VGA cable, or the like.

As shown in FIGS. 1A and 1B, the target recognition, analysis, and tracking system 10 may be used to recognize, analyze, and/or track a human target such as the user 18. For example, the user 18 may be tracked using the capture device 20 such that the gestures and/or movements of user 18 may be captured to animate an avatar or on-screen character and/or may be interpreted as controls that may be used to affect the application being executed by computing environment 12. Thus, according to one embodiment, the user 18 may move his or her body to control the application and/or animate the avatar or on-screen character.

As shown in FIGS. 1A and 1B, in an example embodiment, the application executing on the computing environment 12 may be a boxing game that the user 18 may be playing. For example, the computing environment 12 may use the audiovisual device 16 to provide a visual representation of a boxing opponent 38 to the user 18. The computing environment 12 may also use the audiovisual device 16 to provide a visual representation of a player avatar 40 that the user 18 may control with his or her movements. For example, as shown in FIG. 1B, the user 18 may throw a punch in physical space to cause the player avatar 40 to throw a punch in game space. Thus, according to an example embodiment, the computer environment 12 and the capture device 20 of the target recognition, analysis, and tracking system 10 may be used to recognize and analyze the punch of the user 18 in physical space such that the punch may be interpreted as a game control of the player avatar 40 in game space and/or the motion of the punch may be used to animate the player avatar 40 in game space.

Other movements by the user 18 may also be interpreted as other controls or actions and/or used to animate the player avatar, such as controls to bob, weave, shuffle, block, jab, or throw a variety of different power punches. Furthermore, some movements may be interpreted as controls that may correspond to actions other than controlling the player avatar 40. For example, in one embodiment, the player may use movements to end, pause, or save a game, select a level, view high scores, communicate with a friend, etc. According to another embodiment, the player may use movements to select the game or other application from a main user interface. Thus, in example embodiments, a full range of motion of the user 18 may be available, used, and analyzed in any suitable manner to interact with an application.

In example embodiments, the human target such as the user 18 may have an object. In such embodiments, the user of an electronic game may be holding the object such that the motions of the player and the object may be used to adjust and/or control parameters of the game. For example, the motion of a player holding a racket may be tracked and utilized for controlling an on-screen racket in an electronic sports game. In another example embodiment, the motion of a player holding an object may be tracked and utilized for controlling an on-screen weapon in an electronic combat game.

According to other example embodiments, the target recognition, analysis, and tracking system 10 may further be used to interpret target movements as operating system and/or application controls that are outside the realm of games. For example, virtually any controllable aspect of an operating system and/or application may be controlled by movements of the target such as the user 18.

FIG. 2 illustrates an example embodiment of the capture device 20 that may be used in the target recognition, analysis, and tracking system 10. According to an example embodiment, the capture device 20 may be configured to capture video with depth information including a depth image that may include depth values via any suitable technique including, for example, time-of-flight, structured light, stereo image, or the like. According to one embodiment, the capture device 20 may organize the depth information into “Z layers,” or layers that may be perpendicular to a Z axis extending from the depth camera along its line of sight.

As shown in FIG. 2, the capture device 20 may include an image camera component 22. According to an example embodiment, the image camera component 22 may be a depth camera that may capture the depth image of a scene. The depth image may include a two-dimensional (2-D) pixel area of the captured scene where each pixel in the 2-D pixel area may represent a depth value such as a length or distance in, for example, centimeters, millimeters, or the like of an object in the captured scene from the camera.

As shown in FIG. 2, according to an example embodiment, the image camera component 22 may include an IR light component 24, a three-dimensional (3-D) camera 26, and an RGB camera 28 that may be used to capture the depth image of a scene. For example, in time-of-flight analysis, the IR light component 24 of the capture device 20 may emit an infrared light onto the scene and may then use sensors (not shown) to detect the backscattered light from the surface of one or more targets and objects in the scene using, for example, the 3-D camera 26 and/or the RGB camera 28. In some embodiments, pulsed infrared light may be used such that the time between an outgoing light pulse and a corresponding incoming light pulse may be measured and used to determine a physical distance from the capture device 20 to a particular location on the targets or objects in the scene. Additionally, in other example embodiments, the phase of the outgoing light wave may be compared to the phase of the incoming light wave to determine a phase shift. The phase shift may then be used to determine a physical distance from the capture device to a particular location on the targets or objects.

According to another example embodiment, time-of-flight analysis may be used to indirectly determine a physical distance from the capture device 20 to a particular location on the targets or objects by analyzing the intensity of the reflected beam of light over time via various techniques including, for example, shuttered light pulse imaging.

In another example embodiment, the capture device 20 may use a structured light to capture depth information. In such an analysis, patterned light (i.e., light displayed as a known pattern such as grid pattern or a stripe pattern) may be projected onto the scene via, for example, the IR light component 24. Upon striking the surface of one or more targets or objects in the scene, the pattern may become deformed in response. Such a deformation of the pattern may be captured by, for example, the 3-D camera 26 and/or the RGB camera 28 and may then be analyzed to determine a physical distance from the capture device to a particular location on the targets or objects.

According to another embodiment, the capture device 20 may include two or more physically separated cameras that may view a scene from different angles to obtain visual stereo data that may be resolved to generate depth information.

The capture device 20 may further include a microphone 30. The microphone 30 may include a transducer or sensor that may receive and convert sound into an electrical signal. According to one embodiment, the microphone 30 may be used to reduce feedback between the capture device 20 and the computing environment 12 in the target recognition, analysis, and tracking system 10. Additionally, the microphone 30 may be used to receive audio signals that may also be provided by the user to control applications such as game applications, non-game applications, or the like that may be executed by the computing environment 12.

In an example embodiment, the capture device 20 may further include a processor 32 that may be in operative communication with the image camera component 22. The processor 32 may include a standardized processor, a specialized processor, a microprocessor, or the like that may execute instructions including, for example, instructions for receiving a depth image; generating a grid of voxels based on the depth image; determining whether a location or position has been estimated for an extremity of a human target included the grid of voxels; adjusting a body part of a model associated with the extremity to the location or position when, based on the determination, the location or position has been estimated for the extremity; and adjusting the body part of the model to a closest voxel associated with the human target when, based on the determination, the location or position has not been estimated for the extremity, or any other suitable instruction, which will be described in more detail below.

The capture device 20 may further include a memory component 34 that may store the instructions that may be executed by the processor 32, images or frames of images captured by the 3-D camera or RGB camera, or any other suitable information, images, or the like. According to an example embodiment, the memory component 34 may include random access memory (RAM), read only memory (ROM), cache, Flash memory, a hard disk, or any other suitable storage component. As shown in FIG. 2, in one embodiment, the memory component 34 may be a separate component in communication with the image camera component 22 and the processor 32. According to another embodiment, the memory component 34 may be integrated into the processor 32 and/or the image capture component 22.

As shown in FIG. 2, the capture device 20 may be in communication with the computing environment 12 via a communication link 36. The communication link 36 may be a wired connection including, for example, a USB connection, a Firewire connection, an Ethernet cable connection, or the like and/or a wireless connection such as a wireless 802.11b, g, a, or n connection. According to one embodiment, the computing environment 12 may provide a clock to the capture device 20 that may be used to determine when to capture, for example, a scene via the communication link 36.

Additionally, the capture device 20 may provide the depth information and images captured by, for example, the 3-D camera 26 and/or the RGB camera 28, and/or a skeletal model that may be generated by the capture device 20 to the computing environment 12 via the communication link 36. The computing environment 12 may then use the model, depth information, and captured images to, for example, control an application such as a game or word processor and/or animate an avatar or on-screen character. For example, as shown, in FIG. 2, the computing environment 12 may include a gestures library 190. The gestures library 190 may include a collection of gesture filters, each comprising information concerning a gesture that may be performed by the skeletal model (as the user moves). The data captured by the cameras 26, 28 and the capture device 20 in the form of the skeletal model and movements associated with it may be compared to the gesture filters in the gestures library 190 to identify when a user (as represented by the skeletal model) has performed one or more gestures. Those gestures may be associated with various controls of an application. Thus, the computing environment 12 may use the gestures library 190 to interpret movements of the skeletal model and to control an application based on the movements.

FIG. 3 illustrates an example embodiment of a computing environment that may be used to interpret one or more gestures in a target recognition, analysis, and tracking system and/or animate an avatar or on-screen character displayed by the target recognition, analysis, and tracking system. The computing environment such as the computing environment 12 described above with respect to FIGS. 1A-2 may be a multimedia console 100, such as a gaming console. As shown in FIG. 3, the multimedia console 100 has a central processing unit (CPU) 101 having a level 1 cache 102, a level 2 cache 104, and a flash ROM (Read Only Memory) 106. The level 1 cache 102 and a level 2 cache 104 temporarily store data and hence reduce the number of memory access cycles, thereby improving processing speed and throughput. The CPU 101 may be provided having more than one core, and thus, additional level 1 and level 2 caches 102 and 104. The flash ROM 106 may store executable code that is loaded during an initial phase of a boot process when the multimedia console 100 is powered ON.

A graphics processing unit (GPU) 108 and a video encoder/video codec (coder/decoder) 114 form a video processing pipeline for high speed and high resolution graphics processing. Data is carried from the graphics-processing unit 108 to the video encoder/video codec 114 via a bus. The video processing pipeline outputs data to an A/V (audio/video) port 140 for transmission to a television or other display. A memory controller 110 is connected to the GPU 108 to facilitate processor access to various types of memory 112, such as, but not limited to, a RAM (Random Access Memory).

The multimedia console 100 includes an I/O controller 120, a system management controller 122, an audio processing unit 123, a network interface controller 124, a first USB host controller 126, a second USB controller 128 and a front panel I/O subassembly 130 that are preferably implemented on a module 118. The USB controllers 126 and 128 serve as hosts for peripheral controllers 142(1)-142(2), a wireless adapter 148, and an external memory device 146 (e.g., flash memory, external CD/DVD ROM drive, removable media, etc.). The network interface controller 124 and/or wireless adapter 148 provide access to a network (e.g., the Internet, home network, etc.) and may be any of a wide variety of various wired or wireless adapter components including an Ethernet card, a modem, a Bluetooth module, a cable modem, and the like.

System memory 143 is provided to store application data that is loaded during the boot process. A media drive 144 is provided and may comprise a DVD/CD drive, hard drive, or other removable media drive, etc. The media drive 144 may be internal or external to the multimedia console 100. Application data may be accessed via the media drive 144 for execution, playback, etc. by the multimedia console 100. The media drive 144 is connected to the I/O controller 120 via a bus, such as a Serial ATA bus or other high-speed connection (e.g., IEEE 1394).

The system management controller 122 provides a variety of service functions related to assuring availability of the multimedia console 100. The audio processing unit 123 and an audio codec 132 form a corresponding audio processing pipeline with high fidelity and stereo processing. Audio data is carried between the audio processing unit 123 and the audio codec 132 via a communication link. The audio processing pipeline outputs data to the A/V port 140 for reproduction by an external audio player or device having audio capabilities.

The front panel I/O subassembly 130 supports the functionality of the power button 150 and the eject button 152, as well as any LEDs (light emitting diodes) or other indicators exposed on the outer surface of the multimedia console 100. A system power supply module 136 provides power to the components of the multimedia console 100. A fan 138 cools the circuitry within the multimedia console 100.

The CPU 101, GPU 108, memory controller 110, and various other components within the multimedia console 100 are interconnected via one or more buses, including serial and parallel buses, a memory bus, a peripheral bus, and a processor or local bus using any of a variety of bus architectures. By way of example, such architectures can include a Peripheral Component Interconnects (PCI) bus, PCI-Express bus, etc.

When the multimedia console 100 is powered ON, application data may be loaded from the system memory 143 into memory 112 and/or caches 102, 104 and executed on the CPU 101. The application may present a graphical user interface that provides a consistent user experience when navigating to different media types available on the multimedia console 100. In operation, applications and/or other media contained within the media drive 144 may be launched or played from the media drive 144 to provide additional functionalities to the multimedia console 100.

The multimedia console 100 may be operated as a standalone system by simply connecting the system to a television or other display. In this standalone mode, the multimedia console 100 allows one or more users to interact with the system, watch movies, or listen to music. However, with the integration of broadband connectivity made available through the network interface 124 or the wireless adapter 148, the multimedia console 100 may further be operated as a participant in a larger network community.

When the multimedia console 100 is powered ON, a set amount of hardware resources are reserved for system use by the multimedia console operating system. These resources may include a reservation of memory (e.g., 16 MB), CPU and GPU cycles (e.g., 5%), networking bandwidth (e.g., 8 kbs), etc. Because these resources are reserved at system boot time, the reserved resources do not exist from the application's view.

In particular, the memory reservation preferably is large enough to contain the launch kernel, concurrent system applications and drivers. The CPU reservation is preferably constant such that if the reserved CPU usage is not used by the system applications, an idle thread will consume any unused cycles.

With regard to the GPU reservation, lightweight messages generated by the system applications (e.g., popups) are displayed by using a GPU interrupt to schedule code to render popup into an overlay. The amount of memory required for an overlay depends on the overlay area size and the overlay preferably scales with screen resolution. Where a full user interface is used by the concurrent system application, it is preferable to use a resolution independent of application resolution. A scaler may be used to set this resolution such that the need to change frequency and cause a TV resynch is eliminated.

After the multimedia console 100 boots and system resources are reserved, concurrent system applications execute to provide system functionalities. The system functionalities are encapsulated in a set of system applications that execute within the reserved system resources described above. The operating system kernel identifies threads that are system application threads versus gaming application threads. The system applications are preferably scheduled to run on the CPU 101 at predetermined times and intervals in order to provide a consistent system resource view to the application. The scheduling is to minimize cache disruption for the gaming application running on the console.

When a concurrent system application requires audio, audio processing is scheduled asynchronously to the gaming application due to time sensitivity. A multimedia console application manager (described below) controls the gaming application audio level (e.g., mute, attenuate) when system applications are active.

Input devices (e.g., controllers 142(1) and 142(2)) are shared by gaming applications and system applications. The input devices are not reserved resources, but are to be switched between system applications and the gaming application such that each will have a focus of the device. The application manager preferably controls the switching of input stream, without knowledge the gaming application's knowledge and a driver maintains state information regarding focus switches. The cameras 26, 28 and capture device 20 may define additional input devices for the multimedia console 100.

FIG. 4 illustrates another example embodiment of a computing environment 220 that may be the computing environment 12 shown in FIGS. 1A-2 used to interpret one or more gestures in a target recognition, analysis, and tracking system and/or animate an avatar or on-screen character displayed by a target recognition, analysis, and tracking system. The computing environment 220 is only one example of a suitable computing environment and is not intended to suggest any limitation as to the scope of use or functionality of the presently disclosed subject matter. Neither should the computing environment 220 be interpreted as having any dependency or requirement relating to any one or combination of components illustrated in the exemplary computing environment 220. In some embodiments the various depicted computing elements may include circuitry configured to instantiate specific aspects of the present disclosure. For example, the term circuitry used in the disclosure can include specialized hardware components configured to perform function(s) by firmware or switches. In other examples embodiments the term circuitry can include a general-purpose processing unit, memory, etc., configured by software instructions that embody logic operable to perform function(s). In example embodiments where circuitry includes a combination of hardware and software, an implementer may write source code embodying logic and the source code can be compiled into machine-readable code that can be processed by the general-purpose processing unit. Since one skilled in the art can appreciate that the state of the art has evolved to a point where there is little difference between hardware, software, or a combination of hardware/software, the selection of hardware versus software to effectuate specific functions is a design choice left to an implementer. More specifically, one of skill in the art can appreciate that a software process can be transformed into an equivalent hardware structure, and a hardware structure can itself be transformed into an equivalent software process. Thus, the selection of a hardware implementation versus a software implementation is one of design choice and left to the implementer.

In FIG. 4, the computing environment 220 comprises a computer 241, which typically includes a variety of computer readable media. Computer readable media can be any available media that can be accessed by computer 241 and includes both volatile and nonvolatile media, removable and non-removable media. The system memory 222 includes computer storage media in the form of volatile and/or nonvolatile memory such as read only memory (ROM) 223 and random access memory (RAM) 260. A basic input/output system 224 (BIOS), containing the basic routines that help to transfer information between elements within computer 241, such as during start-up, is typically stored in ROM 223. RAM 260 typically contains data and/or program modules that are immediately accessible to and/or presently being operated on by processing unit 259. By way of example, and not limitation, FIG. 4 illustrates operating system 225, application programs 226, other program modules 227, and program data 228.

The computer 241 may also include other removable/non-removable, volatile/nonvolatile computer storage media. By way of example only, FIG. 4 illustrates a hard disk drive 238 that reads from or writes to non-removable, nonvolatile magnetic media, a magnetic disk drive 239 that reads from or writes to a removable, nonvolatile magnetic disk 254, and an optical disk drive 240 that reads from or writes to a removable, nonvolatile optical disk 253 such as a CD ROM or other optical media. Other removable/non-removable, volatile/nonvolatile computer storage media that can be used in the exemplary operating environment include, but are not limited to, magnetic tape cassettes, flash memory cards, digital versatile disks, digital video tape, solid state RAM, solid state ROM, and the like. The hard disk drive 238 is typically connected to the system bus 221 through a non-removable memory interface such as interface 234, and magnetic disk drive 239 and optical disk drive 240 are typically connected to the system bus 221 by a removable memory interface, such as interface 235.

The drives and their associated computer storage media discussed above and illustrated in FIG. 4, provide storage of computer readable instructions, data structures, program modules and other data for the computer 241. In FIG. 4, for example, hard disk drive 238 is illustrated as storing operating system 258, application programs 257, other program modules 256, and program data 255. Note that these components can either be the same as or different from operating system 225, application programs 226, other program modules 227, and program data 228. Operating system 258, application programs 257, other program modules 256, and program data 255 are given different numbers here to illustrate that, at a minimum, they are different copies. A user may enter commands and information into the computer 241 through input devices such as a keyboard 251 and pointing device 252, commonly referred to as a mouse, trackball or touch pad. Other input devices (not shown) may include a microphone, joystick, game pad, satellite dish, scanner, or the like. These and other input devices are often connected to the processing unit 259 through a user input interface 236 that is coupled to the system bus, but may be connected by other interface and bus structures, such as a parallel port, game port or a universal serial bus (USB). The cameras 26, 28 and capture device 20 may define additional input devices for the console 100. A monitor 242 or other type of display device is also connected to the system bus 221 via an interface, such as a video interface 232. In addition to the monitor, computers may also include other peripheral output devices such as speakers 244 and printer 243, which may be connected through an output peripheral interface 233.

The computer 241 may operate in a networked environment using logical connections to one or more remote computers, such as a remote computer 246. The remote computer 246 may be a personal computer, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to the computer 241, although only a memory storage device 247 has been illustrated in FIG. 4. The logical connections depicted in FIG. 2 include a local area network (LAN) 245 and a wide area network (WAN) 249, but may also include other networks. Such networking environments are commonplace in offices, enterprise-wide computer networks, intranets and the Internet.

When used in a LAN networking environment, the computer 241 is connected to the LAN 245 through a network interface or adapter 237. When used in a WAN networking environment, the computer 241 typically includes a modem 250 or other means for establishing communications over the WAN 249, such as the Internet. The modem 250, which may be internal or external, may be connected to the system bus 221 via the user input interface 236, or other appropriate mechanism. In a networked environment, program modules depicted relative to the computer 241, or portions thereof, may be stored in the remote memory storage device. By way of example, and not limitation, FIG. 4 illustrates remote application programs 248 as residing on memory storage device 247. It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers may be used.

FIG. 5 depicts a flow diagram of an example method 300 for tracking a user in a scene. The example method 300 may be implemented using, for example, the capture device 20 and/or the computing environment 12 of the target recognition, analysis, and tracking system 10 described with respect to FIGS. 1A-4. In an example embodiment, the example method 300 may take the form of program code (i.e., instructions) that may be executed by, for example, the capture device 20 and/or the computing environment 12 of the target recognition, analysis, and tracking system 10 described with respect to FIGS. 1A-4.

According to one embodiment, at 305, a depth image may be received. For example, the target recognition, analysis, and tracking system may include a capture device such as the capture device 20 described above with respect to FIGS. 1A-2. The capture device may capture or observe a scene that may include one or more targets. In an example embodiment, the capture device may be a depth camera configured to obtain an image such as a depth image of the scene using any suitable technique such as time-of-flight analysis, structured light analysis, stereo vision analysis, or the like.

The depth image may be a plurality of observed pixels where each observed pixel has an observed depth value. For example, the depth image may include a two-dimensional (2-D) pixel area of the captured scene where each pixel in the 2-D pixel area may have a depth value such as a length or distance in, for example, centimeters, millimeters, or the like of an object in the captured scene from the capture device.

FIG. 6 illustrates an example embodiment of a depth image 400 that may be received at 305. According to an example embodiment, the depth image 400 may be an image or frame of a scene captured by, for example, the 3-D camera 26 and/or the RGB camera 28 of the capture device 20 described above with respect to FIG. 2. As shown in FIG. 6, the depth image 400 may include a human target 402 a corresponding to, for example, a user such as the user 18 described above with respect to FIGS. 1A and 1B and one or more non-human targets 404 such as a wall, a table, a monitor, or the like in the captured scene. As described above, the depth image 400 may include a plurality of observed pixels where each observed pixel has an observed depth value associated therewith. For example, the depth image 400 may include a two-dimensional (2-D) pixel area of the captured scene where each pixel at a particular X-value and Y-value in the 2-D pixel area may have a depth value such as a length or distance in, for example, centimeters, millimeters, or the like of a target or object in the captured scene from the capture device.

In one embodiment, the depth image 400 may be colorized such that different colors of the pixels of the depth image correspond to and/or visually depict different distances of the human target 402 a and non-human targets 404 from the capture device. For example, the pixels associated with a target closest to the capture device may be colored with shades of red and/or orange in the depth image whereas the pixels associated with a target further away may be colored with shades of green and/or blue in the depth image.

Referring back to FIG. 5, in one embodiment, upon receiving the image, at 305, one or more high-variance and/or noisy depth values may be removed and/or smoothed from the depth image; portions of missing and/or removed depth information may be filled in and/or reconstructed; and/or any other suitable processing may be performed on the received depth image may such that the depth information associated with the depth image may used to generate a model such as a skeletal model, which will be described in more detail below.

According to an example embodiment, at 310, a grid of one or more voxels may be generated based on the received depth image. For example, the target recognition, analysis, and tracking system may downsample the received depth image by generating one or more voxels using information included in the received depth image such that a downsampled depth image may be generated. In one embodiment, the one or more voxels may be volume elements that may represent data or values of the information included in the received depth image on a sub-sampled grid.

For example, as described above, the depth image may include a 2-D pixel area of the captured scene where each pixel may have an X-value, a Y-value, and a depth value (or Z-value) associated therewith. In one embodiment, the depth image may be downsampled by reducing the pixels in the 2-D pixel area into a grid of one or more voxels. For example, the depth image may be divided into portions or blocks of pixels such as 4×4 blocks of pixels, 5×5 blocks of pixels, 8×8 block of pixels, a 10×10 block of pixels, or the like. Each portion or block may be processed to generate a voxel for the depth image that may represent a position of the portion or block associated the pixels of the 2-D depth image in real-world space. According to an example embodiment, the position of each voxel may be generated based on, for example, an average depth value of the valid or non-zero depth values for the pixels in the block or portion that the voxel may represent, a minimum, maximum, and/or a median depth value of the pixels in the portion or block that the voxel may represent, an average of the X-values and Y-values for pixels having a valid depth value in the portion or the block that the voxel may represent, or any other suitable information provided by the depth image. Thus, according to an example embodiment, each voxel may represent a sub-volume portion or block of the depth image having values such as an average depth value of the valid or non-zero depth values for the pixels in the block or portion that the voxel may represent, a minimum, maximum, and/or a median depth value of the pixels in the portion or block that the voxel may represent, an average of the X-values and Y-values for pixels having a valid depth value in the portion or the block that the voxel may represent, or any other suitable information provided by the depth image based on the X-values, Y-values, and depth values of the corresponding portion or block of pixels of the depth image received at 305.

In one embodiment, the grid of the one or more voxels in the downsampled depth image may be layered. For example, the target recognition, analysis, and tracking system may generate voxels as described above. The target recognition, analysis, and tracking system may then stack a generated voxel over one or more other generated voxels in the grid.

According to an example embodiment, the target recognition, analysis, and tracking system may stack voxels in the grid around, for example, edges of objects in the scene that may be captured in the depth image. For example, a depth image received at 305 may include a human target and a non-human target such as a wall. The human target may overlap the non-human target such as the wall at, for example, an an edge of the human target. In one embodiment, the overlapping edge may include information such as depth values, X-values, Y-values, or the like associated with the human target and the non-human target that may be captured in the depth image. The target recognition, analysis, and tracking system may generate a voxel associated with the human target and a voxel associated with the non-human target at the overlapping edge such that the voxels may be stacked and the information such as depth values, X-values, Y-values, or the like of the overlapping edge may be retained in the grid.

According to another embodiment, the grid of one or more voxels may be generated at 310 by projecting, for example, information such as the depth values, X-values, Y-values, or the like into three-dimensional (3-D) space. For example, depth values may be mapped to 3-D points in the 3-D space using a transformation such as a camera, image, or perspective transform such that the information may be transformed as trapezoidal or pyramidal shapes in the 3-D space. In one embodiment, the 3-D space having the trapezoidal or pyramidal shapes may be divided into blocks such as cubes that may create a grid of voxels such that each of the blocks or cubes may represent a voxel in the grid. For example, the target recognition, analysis, and tracking system may superimpose a 3-D grid over the 3-D points that correspond to the object in the depth image. The target recognition, analysis, and tracking system may then divide or chop up the grid into the blocks representing voxels to downsample the depth image into a lower resolution. According to an example embodiment, each of the voxels in the grid may include an average depth value of the valid or non-zero depth values for the pixels associated with the 3-D space in the grid. This may allow the voxel to represent a minimum and/or maximum depth value of the pixels associated with the 3-D space in the grid; an average of the X-values and Y-values for pixels having a valid depth value associated with the 3-D space; or any other suitable information provided by the depth image.

FIGS. 7A-7B illustrate an example embodiment of a portion of the depth image being downsampled. For example, as shown in FIG. 7A, a portion 410 of the depth image 400 described above with respect to FIG. 6 may include a plurality of pixels 420 where each pixel 420 may have an X-value, a Y-value, and a depth value (or Z-value) associated therewith. According to one embodiment, as described above, a depth image such as the depth image 400 may be downsampled by reducing the pixels in the 2-D pixel area into a grid of one or more voxels. For example, as shown in FIG. 7A, the portion 410 of the depth image 400 may be divided into a portion or a block 430 of the pixels 420 such as 8×8 block of the pixels 420. The target recognition, analysis, and tracking system may process the portion or block 430 to generate a voxel 440 that may represent a position of the portion or block 430 associated the pixels 420 in real-world space as shown in FIGS. 7A-7B.

Referring back to FIG. 5, at 315, the background may be removed from the downsampled depth image. For example, a background such as the non-human targets or objects in the downsampled depth image may be removed to isolate foreground objects such as a human target associated with a user. As described above, the target recognition, analysis, and tracking system may downsample a captured or observed depth image by generating a grid of one or more voxels for the captured or observed depth image. The target recognition, analysis, and tracking system may analyze each of the voxels in the downsampled depth image to determine whether a voxel may be associated with a background object such as one or more non-human targets of the depth image. If a voxel may be associated with a background object, the voxel may be removed or discarded from the downsampled depth image such that a foreground object, such as the human target, and the one or more voxels in the grid associated with the foreground object may be isolated.

At 320, one or more extremities such as one or more body parts may be determined or estimated for the isolated foreground object such as the human target. For example, in one embodiment, the target recognition, analysis, and tracking system may apply one or more heuristics or rules to the isolated human target to determine, for example, a centroid or center, a head, shoulders, a torso, arms, legs, or the like associated with the isolated human target. According to one embodiment, based on the determination of the extremities, the target recognition, analysis, and tracking system may generate and/or track or adjust a model of the isolated human target. For example, if the depth image received at 305 may be included in an initial frame observed or captured by a capture device such as the capture device 20 described above with respect to FIGS. 1A-2, a model may be generated based on the location of the extremities such as the centroid, head, shoulders, arms, hands, legs, or the like determined at 320 by, for example, assigning a joint of the skeletal model to the determined locations of the extremities, which will be described in more detail below. Alternatively, if the depth image may be included in a subsequent or non-initial frame observed or captured by the capture device, a model that may have been previously generated may be tracked or adjusted based on the location of the extremities such as the centroid, head, shoulders, arms, hands, legs, or the like determined at 320, which will be described in more detail below.

According to an example embodiment, upon isolating the foreground object such as the human target at 315, the target recognition, analysis, and tracking system may calculate an average of the voxels in the human target to, for example, estimate a centroid or center of the human target at 320. For example, the target recognition, analysis, and tracking system may calculate an average position of the voxels included in the human target that may provide an estimate of the centroid or center of the human target. In one embodiment, the target recognition, analysis, and tracking system may calculate the average position of the voxels associated with the human target based on X-values, Y-values, and depth values associated with the voxels. For example, as described above, the target recognition, analysis, and tracking system may calculate an X-value for a voxel by averaging the X-values of the pixels associated with the voxel, a Y-value for the voxel by averaging the Y-values of the pixels associated with the voxel, and a depth value for the voxel by averaging the depth values of the pixels associated with the voxel. At 320, the target recognition, analysis, and tracking system may average the X-values, the Y-values, and the depth values of the voxels included in the human target to calculate the average position that may provide the estimate of the centroid or center of the human target.

The target recognition, analysis, and tracking system may then determine a head of the human target at 320. For example, in one embodiment, the target recognition, analysis, and tracking system may determine a position or location of the head by searching for various candidates at positions or locations suitable for the head. According to one embodiment, the target recognition, analysis, and tracking system may search for an absolute highest voxel of the human target and/or voxels adjacent to or near the absolute highest voxel, one or more incremental voxels based on the location of the head determined for a previous frame, a highest voxel on an upward vector that may extend vertically from, for example, the centroid or center and/or voxels adjacent or near the highest voxel determined for a previous frame, a highest voxel on a previous upward vector between a center or centroid and a highest voxel determined for a previous frame, or any other suitable voxels to determine a candidate for the extremity such as the head.

The target recognition, analysis, and tracking system may then score the candidates. In an example embodiment, the candidates may be scored based 3-D pattern matching. For example, the target recognition, analysis, and tracking system may create a head cylinder and a shoulder cylinder. The target recognition, analysis, and tracking system may then calculate a score for the candidates based on the number of voxels associated with the candidates that may be included in the head cylinder and/or shoulder cylinders.

According to one embodiment, if a score associated with one of the candidate exceeds a head threshold score, the target recognition, analysis, and tracking system may determine a position or location of the head based on the voxels associated with the candidate at 320. Additionally, if more than one candidate exceeds the head threshold score, the target recognition, analysis, and tracking system may select the candidate that may have the highest score and may then determine the position or location of the extremity such as the head based on the voxels associated with the candidate that may have the highest score. none of the scores associated with the candidates exceeds the head threshold score, the target recognition, analysis, and tracking system may use a previous position or location of the head determined for voxels included in a human target associated with a depth image of a previous frame in which the head score may have exceed the head threshold score or the target recognition, analysis, and tracking system may use a default position or location for a head in a default pose of a human target such as a T-pose, a natural standing pose or the like, if the depth image received at 305 may be in an initial frame captured or observed by the capture device.

According to another embodiment, the target recognition, analysis, and tracking system may include one or more two-dimensional (2-D) patterns associated with, for example, a head shape. The target recognition, analysis, and tracking system may then score the candidates associated with the head based on a likelihood that the voxels associated with the candidates may may be similar to the head shapes of the one or more 2-D patterns. For example, the target recognition, analysis, and tracking system may determine and sample depths values of adjacent or nearby voxels that may be indicative of defining an extremity shape such as a head shape such that a score may be calculated based on a likelihood the sampled depth values of adjacent or nearby voxels may be indicative of one or more of the head shapes of the 2-D patterns.

The target recognition, analysis, and tracking system may further determine the shoulders and hips of the human target at 320. For example, in one embodiment, after determining the location or position of the head of the human target, the target recognition, analysis, and tracking system may determine a location or a position of the shoulders and the hips of the human target. The target recognition, analysis, and tracking system may also determine an orientation of the shoulders and the hips such as a rotation or angle of the shoulders and the hips. For example, the target recognition, analysis, and tracking system may define a head-to-center vector based on the location or positions of the head and center determined or estimated at 320. The target recognition, analysis, and tracking system may then determine or estimate the location or position of the shoulders and/or hips by defining respective volume boxes around a displacement value from a body landmark such as the head or center along the head-to-center vector. The target recognition, analysis, and tracking system may then analyze the voxels included in the respective volume boxes to estimate a location and position of, for example, joints associated the shoulders and/or hips as well as an orientation of the shoulders and/or hips. For example, the target recognition, analysis, and tracking system may calculate a line of best fit for the depth values of the voxels in the respective volume boxes including any mirrored depth values to define respective slopes of the shoulders and/or hips, may search in each direction along the respective slopes to detect edges and may assign joints of the shoulders and/or hips based on a displacement from the edges, or may perform any other suitable technique that may be used to determine or estimate the location or position of the shoulders or hips.

In one example embodiment, the target recognition, analysis, and tracking system may further determine the torso of the human target at 320. For example, after determining the shoulders and the hips, the target recognition, analysis, and tracking system may generate or create a torso volume that may include the voxel associated with and surrounding the head, the shoulders, the center, and the hips. The torso volume may be a cylinder, a pill shape such as a cylinder with rounded ends, or the like based on the location or position of the center, the head, the shoulders, and/or the hips.

According to one embodiment, the target recognition, analysis, and tracking system may create a cylinder that may represent the torso volume having dimensions based on the shoulders, the head, the hips, the center, or the like. For example, the target recognition, analysis, and tracking system may create a cylinder that may have a width or a diameter based on the width of the shoulders and a height based on the distance between the head and the hips. The target recognition, analysis, and tracking system may then orient or angle the cylinder that may represent the torso volume along the head-to-center vector such that the torso volume may reflect the orientation such as the angle of the torso of the human target.

The target recognition, analysis, and tracking system may then estimate or determine the limbs of the human target at 320. For example, the target recognition, analysis, and tracking system may coarsely label voxels outside the torso volume as a limb after generating or creating the torso volume. In one embodiment, the target recognition, analysis, and tracking system may identify each of the voxels outside of the torso volume such that the target recognition, analysis, and tracking system may label the voxels as being part of a limb.

The target recognition, analysis, and tracking system may then determine the actual limbs such as a right and left arm, a right and left hand, a right and left leg, a right and left foot, or the like associated with the voxels outside of the torso volume. In one embodiment, to determine the actual limbs, the target recognition, analysis, and tracking system may compare a previous position or location of an identified limb such as the previous position or location of the right arm, left arm, left leg, right leg, or the like with the position or location of the voxels outside of the torso volume. According to example embodiments, the previous location or position of the previously identified limbs may be a location or position of a limb in a depth image received in a previous frame, a projected body part location or position based on a previous movement, or any other suitable previous location or position of a representation of a human target such as a fully articulated skeleton or volumetric model of the human target. Based on the comparison, the target recognition, analysis, and tracking system may then associate the voxels outside of the torso volume with the closest previously identified limbs. For example, the target recognition, analysis, and tracking system may compare the position or location including the X-value, Y-value, and depth value of each of the voxels outside of the torso volume with the previous positions or locations including the X-values, Y-values, and depth values of the previously identified limbs such as the previously identified left arm, right arm, left leg, right leg, or the like. The target recognition, analysis, and tracking system may then associate each of the voxels outside the torso volume with the previously identified limb that may have the closest location or position based on the comparison.

In another embodiment, to determine the actual limbs, the target recognition, analysis, and tracking system may compare a default position or location of an identified limb such as the right arm, left arm, right leg, left leg, or the like in a default pose of a representation of a human target with the position or location of the voxels outside of the torso volume. For example, the depth image received at 305 may be included in an initial frame captured or observed by the capture device. If the depth image received at 305 may be included in an initial frame, the target recognition, analysis, and tracking may compare a default position or location of a limb such as the default position or location of a right arm, left arm, left leg, right leg, or the like with the position or location of the voxels outside of the torso volume. According to example embodiments, the default location or position of the identified limbs may be a location or position of a limb in a default pose such as a T-pose, a Di Vinci pose, a natural pose, or the like of a representation of a human target such as a fully articulated skeleton or volumetric model of the human target in the default pose. Based on the comparison, the target recognition, analysis, and tracking system may then associate the voxels outside of the torso volume with the closest limb associated with the default pose. For example, the target recognition, analysis, and tracking system may compare the position or location including the X-value, Y-value, and depth value of each of the voxels outside of the torso volume with the default positions or locations including the X-values, Y-values, and depth values of the default limbs such as the default left arm, right arm, left leg, right leg, or the like. The target recognition, analysis, and tracking system may then associate each of the voxels outside the torso volume with the default limb that may have the closest location or position based on the comparison.

The target recognition, analysis, and tracking system may also re-label voxels within the torso volume based on the estimated limbs. For example, in one embodiment, at least a portion of an arm such as a left forearm may be positioned in front of the torso of the human target. Based on the previous position or location of the identified arm, the target recognition, analysis, and tracking system may determine or estimate the portion as being associated with the arm as described above. For example, the previous position or location of the previously identified limb may indicate that the one or more voxels of a limb such as an arm of the human target may be within the torso volume. The target recognition, analysis, and tracking system may then compare the previous positions or locations including the X-values, Y-values, and depth values of the previously identified limbs such as the previously identified left arm, right arm, left leg, right leg, or the like with the position or location of voxels included in the torso volume. The target recognition, analysis, and tracking system may then associate and re-label each of the voxels inside the torso volume with the previously identified limb that may have the closest location or position based on the comparison.

According to one embodiment, after labeling the voxels associated with the limbs, the target recognition, analysis, and tracking system may determine or estimate the location or position of, for example, portions of the labeled limbs at 320. For example, after labeling the voxels associated with the left arm, the right arm, the left leg, and/or the right leg, the target recognition may determine or estimate the location or position of the hands and/or the elbows of the right and left arms, the knees and/or the feet, the elbows, or the like.

The target recognition, analysis, and tracking system may determine or estimate the location or position of the portions such as the hands, elbows, feet, knees, or the like based on limb averages for each of the limbs. For example, the target recognition, analysis, and tracking system may calculate a left arm average location by adding the X-values for each of the voxels of the associated with the left arm, the Y-values for each of the voxels associated with the left arm, and the depth values for each of the voxels associated with the left arm and dividing the sum of each of the X-values, Y-values, and depth values added together by the total number of voxels associated with the left arm. According to one embodiment, the target recognition, analysis, and tracking system may then define a vector or a line between the left shoulder and the left arm average location such that the vector or the line between the left shoulder and the left arm average location may define a first search direction for the left hand. The target recognition, analysis, and tracking system may then search from the shoulders to along the first search direction defined by the vector or the line for the last valid voxel or last voxel having a valid X-value, Y-value, and/or depth value and may associate the location or position of the last valid voxel with the left hand.

According to another embodiment, the target recognition, analysis, and tracking system may calculate an anchor point. The target recognition, analysis, and tracking system may then define a vector or a line between the anchor point and one or more of the limb averages such as the left arm average location such that the vector or the line between the anchor point and the limb averages such as the left arm average location may define a second search direction for a limb such as the left hand. The target recognition, analysis, and tracking system may then search from the anchor point along the second search direction defined by the vector or the line for the last valid voxel or last voxel having a valid X-value, Y-value, and/or depth value and may associate the location or position of the last valid voxel with the limb such as the left hand.

In an example embodiment, the target recognition, analysis, and tracking system may calculate the location or position of the anchor point based on one or more offsets from other determined extremities such as the head, hips, shoulders, or the like. For example, the target recognition, analysis, and tracking system may calculate the X-value and the depth value for the anchor point by extending the location or position of the shoulder in the respective X-direction and Z-direction by half of the X-value and depth value associated with the location or position of the shoulder. The target recognition, analysis, and tracking system may then mirror the location or position of the X-value and the depth value for the anchor point around the extended locations or positions.

The target recognition, analysis, and tracking system may calculate the Y-value for the anchor point based on a displacement of the limb averages locations such as the left arm average location from the head and/or the hips. For example, the target recognition, analysis, and tracking system may calculate the displacement or the difference between the Y-value of the head and the Y-value of the left arm average. The target recognition, analysis, and tracking system may then add the displacement or difference to the Y-value of, for example, the center of the hips to calculate the Y-value of the anchor point.

The target recognition, analysis, and tracking system may also determine or estimate a location or a position of a right hand, a left foot, and a right foot based on a right arm average location, a left leg average location, and a right leg average location respectively and/or one or more anchor points calculated therefore using the techniques described above with respect to the left arm average location and the left hand.

According to an example embodiment, at 320, the target recognition, analysis, and tracking system may also determine or estimate a location or a position of extremities such as elbows and knees based on measurements of the right and left arm average locations and the right and the left leg average locations, other extremities such as the shoulders, the hips, the head, measurements of other extremities calculated as described below, or the like. For example, the target recognition, analysis, and tracking system may determine or estimate the location or position of the left elbow based on the left shoulder, left hand, measurements determined for the left arm as described below, the left arm average, or the like. In one embodiment, the target, recognition, analysis, and tracking system may determine or estimate, for example, the location or position of the left elbow based on the X-value, the Y-value, and the depth value of the left arm average location. For example, the target recognition, analysis, and tracking system may determine the outermost voxels that may define edges associated with the left arm. The target recognition, analysis, and tracking system may then adjust the X-value, the Y-value, and the depth value of the left arm average location to be to be in the middle or equidistance from the edges. The location or position of the left elbow may then be estimated based on the adjusted X-value, Y-value, and depth value.

FIG. 8 depicts an example embodiment of one or more extremities that may determined or estimated for a human target 402 b at 320. As shown in FIG. 8, the target recognition, analysis, and tracking system may estimate or determine a location or position of a head 810, shoulders 816 a-b, a head-to-center vector 812, a centroid 802, hands 822 a-b, hips 818 a-b, feet 824 a-b, or the like for the human target 402 b.

Referring back to FIG. 5, in one embodiment, the target recognition, analysis, and tracking system may then determine whether one or more of the locations or positions determined or estimated for the extremities such as the head, the shoulders, the hips, the hands, the feet, or the like may not have been accurate locations or positions for the actual extremities of the human target at 320. For example, the location or position of the right hand may be inaccurate such that the location or position of the right hand may be stuck on or adjacent to the location or position of the shoulder or the hip.

According to an example embodiment, the target recognition, analysis, and tracking system may include or store a list of volume markers for the various extremities that may indicate inaccurate locations or position of the extremities. For example, the list may include volume markers around the shoulders and the hips that may be associated with the hands. The target recognition, analysis, and tracking system may determine whether the location or position for the hands may be accurate based on the volume markers associated with the hands in the list. For example, if the location or position of a hand may be within one of the volume markers associated with the hand in the list, the target recognition, analysis, and tracking system may determine that the location or position of the hand may be inaccurate. According to one embodiment, the target recognition, analysis, and tracking system may then adjust the location or position of the hand to the previous accurate location of the hand in a previous frame to the current location or position of the hand.

In one example embodiment, at 320, the target recognition, analysis, and tracking system may scan the voxels associated with the isolated human target to determine the dimensions of the extremities associated therewith. For example, the isolated human target may be scanned to determine, for example, geometric constraints or measurements such as lengths, widths, or the like associated with the extremities such as the arms, legs, head, shoulders, hips, torso, or the like.

To determine the dimensions, the target recognition, analysis, and tracking system may generate an estimator of proposed joint dimensions for each of the extremities. The target recognition, analysis, and tracking system may calculate a mean and a standard deviation for each of the proposed joint dimensions using the estimators. The target recognition, analysis, and tracking system may add the proposed joint dimensions within a defined percentage deviation and the outliers or the proposed joint dimensions outside the defined percentage deviation may be rejected. The target recognition, analysis, and tracking system may then determine the dimensions of the extremities based on the estimator that may have a highest ratio between the standard deviation thereof and the number of the proposed joint dimensions.

According to another example embodiment, the target recognition, analysis, and tracking system may use one or more heuristics or rules to determine whether the dimensions determined by the scan may be correct. For example, the target recognition, analysis, and tracking system use a heuristic or rule that may determine whether the Euclidean distance between symmetrical joints may be roughly equivalent, a heuristic or rule that may determine whether the hands and/or elbows near the body, a heuristic and/or rule that may determine whether the head may be locked in a position or location, a heuristic and/or rule that may determine whether the hands close to the head, or the like that may be used to adjust the dimensions.

At 325, the target recognition, analysis, and tracking system may track a model based on the determined or estimated extremities. For example, the target recognition, analysis, and tracking system may generate and/or may include a model such as a skeletal that may have one or more joints and bones defined therebetween.

FIG. 9 illustrates an example embodiment a model 900 such as a skeletal model that may be generated. According to an example embodiment, the model 900 may include one or more data structures that may represent, for example, a three-dimensional model of a human. Each body part may be characterized as a mathematical vector having X, Y, and Z values that may define joints and bones of the model 900.

As shown in FIG. 9, the model 900 may include one or more joints j1-j16. According to an example embodiment, each of the joints j1-j16 may enable one or more body parts defined there between to move relative to one or more other body parts. For example, a model representing a human target may include a plurality of rigid and/or deformable body parts that may be defined by one or more structural members such as “bones” with the joints j1-j16 located at the intersection of adjacent bones. The joints j1-16 may enable various body parts associated with the bones and joints j1-j16 to move independently of each other. For example, the bone defined between the joints j10 and j12, shown in FIG. 9, corresponds to a forearm that may be moved independent of, for example, the bone defined between joints j14 and j16 that corresponds to a calf.

Referring back to FIG. 5, at 325, the target recognition, analysis, and tracking system may adjust one or more body parts such as the joints j1-j16 of the model based on the location or position estimated or determined for the extremities of the human target at 320. For example, the target recognition, analysis, and tracking system may adjust the joint j1 associated with the head to correspond the position or location such as the location or position for the head determined at 320. Thus, in an example embodiment, the joint j1 may be assigned the X-value, the Y-value, and the depth value associated with the location or position estimated or determined for the head, which will be described in more detail below.

Additionally, at 325, the target recognition, analysis, and tracking system may adjust one or more body parts such as the joints j1-j16 of the model using a default location or position for a default pose such as a T-pose, Di Vinci pose, a natural pose, or the like when, for example, the target recognition, analysis, and tracking system may not have determined or estimated locations or positions for one or more extremities of the human target. For example, the target recognition, analysis, and tracking system may relax one or more body parts such as joints j1-j16 of the model to the default location or position in the default pose. The target recognition, analysis, and tracking system may then magnetize the one or more of the body parts such as the joints j1-j16 of the model to the closest voxel of the human target using any suitable technique. For example, in one embodiment, the target recognition, analysis, and tracking system may magnetize the one or more body parts such as the joints j1-j16 such that the one or more body parts may be adjusted to the location or position including, for example, an X-value, Y-value, and/or depth value (or Z-value) of a voxel of the human target that may be closest in, for example, distance to the one or more body parts of the model in the default pose, which will be described in more detail below.

According to additional embodiments, at 325, the target recognition, analysis, and tracking system may adjust the one or more body parts using momentum information calculated or determined for the human target, recent movements associated with the human target, a location or a position of other extremities of the human target, or any other suitable information, values, and/or locations or positions associated with, for example, the human target and/or the voxels in the grid.

FIG. 10 depicts a flow diagram of an example method for tracking a model at 325 shown in FIG. 5. For example, as shown in FIG. 5, after one or more extremities may be determined or estimated at 320, a model may be tracked at 325. To track the model at 325, the target recognition, analysis, and tracking system may perform the method or process described below with respect to FIG. 10 below.

In one embodiment, at 1005, a determination may be made regarding whether a location or position of one or more extremities may have been estimated or determined. For example, the target recognition, analysis, and tracking system may determine whether a location or position of one or more extremities of the human target may have been estimated or determined, for example, at 320 shown in FIG. 5.

Additionally, at 1005, a determination may be made regarding whether a location or a position estimated for the one or more extremities may be valid. According to one embodiment, the target recognition, analysis, and tracking system may determine whether one or more of the locations or positions determined or estimated for the extremities such as the head, the shoulders, the hips, the hands, the feet, or the like may not have been accurate locations or positions for the actual extremities of the human target. For example, as described above, the location or position of the right hand may be inaccurate such that the location or position of the right hand may be stuck on or adjacent to the location or position of the shoulder or the hip. Thus, in one embodiment, the target recognition, analysis, and tracking system may further verify that the locations or positions for the one or more extremities may be valid such that the locations or positions may be accurate for the human target at 1005.

At 1010, when a location or position may have been estimated or determined for one or more extremities and/or the location or position may be valid, one or more body parts of a model associated with the one or more extremities may be adjusted based on the location or position at 1015. According to one embodiment, the target recognition, analysis, and tracking system may adjust the one or more body parts such as the joints j1-j16 of the model based on the location or position estimated or determined for the extremities. For example, the target recognition, analysis, and tracking system may adjust the joint j1 of the model associated with the head to the position or location such as the location or position determined or estimated for the head 810, at 320, as shown in FIG. 8. Thus, in an example embodiment, the joint j1 may be assigned the X-value, the Y-value, and the depth value associated with the location or position estimated or determined for the head 810 as described above. Additionally, the target recognition, analysis, and tracking system may adjust additional body parts such as the joints j2-j16 to a location or position of an extremity such as the hands, feet, elbows, knees, shoulders, hips, or the like associated with the respective joints j2-j16.

At 1010, when a location or position may not have been estimated or determined for one or more extremities of the human target and/or the location or position may not be valid, one or more body parts of the model may be relaxed at 1020. For example, in one embodiment, the target recognition, analysis, and tracking system may relax one or more body parts such as the joints j1-1j16 of the model based on a default location or position in a default pose at 1020. To relax one or more body parts of the model, the target recognition, analysis, and tracking system may adjust the one or more body parts to the default location or position such that that the one or more body parts may return to a neutral pose or default pose such as a T-pose, Di Vinci pose, a natural pose, or the like. Thus, in one embodiment, at 1010, the target recognition, analysis, and tracking system may adjust a body part such as the joint j9-j12 to default location or positions including default X-values, Y-values, and depth values for a left and right elbow and a left and right hand in a default pose when a location or a position may not have been estimated for the left and right elbow and the left and right hand associated with the human target.

At 1025, one or more body parts of the model may then be magnetized to a closest voxel associated with, for example, the human target. For example, in one embodiment, the target recognition, analysis, and tracking system may position the model over the human target in the grid of voxels, at 1025, such that the model may be imposed or overlaid on the human target. The target recognition, analysis, and tracking system may then magnetize or adjust the one or more body parts such as the joints j1-j16 of the model to a location or position of a voxel associated with the human target that may be closest to the default location or position of the one or more body parts. For example, in one embodiment, the target recognition, analysis, and tracking system may adjust the one or more body parts such as the joints j1-j16 at the default location or position to a location or position including, for example, an X-value, Y-value, and/or depth value (or Z-value) of a voxel of the human target that may be the closest distance to the default position or location of the one or more body parts in the default pose such that the one or more body parts may be assigned the X-value, Y-value, and/or depth value of the voxel. According to another embodiment, the one or more joints may be magnetized to one or more voxels based on a surface of the human target. For example, the one or more body parts may be magnetized to voxels that define a boundary or surface of the human target in the scene such that the one or more body parts of the model may adjusted and/or assigned to a location or position close to the boundary or surface of the human target.

At 1030, the model including one or more body parts of the model may be adjusted based on a geometric constraint. For example, in one embodiment, the target recognition, analysis, and tracking system may further adjust a dimension, a location or position, or the like of one or more body parts such as the joints j1-j16 of the model based on a geometric constraint. According to an example embodiment, the geometric constraint may include, for example, measurements or dimensions such as lengths and/or widths, angles, positions, shapes, or the like associated with the extremities of the human target and/or body parts of a typical human. For example, as described above, the target recognition, analysis, and tracking system may scan voxels associated with the human target to determine geometric constraints such as measurements or dimensions, angles, positions, shapes, or the like of the human target and the extremities associated therewith. According to another embodiment, the target recognition, analysis, and tracking system may include geometric constraints such as measurements or dimensions, angles, positions, shapes, or the like of a typical human and typical body parts stored therein. For example, the target recognition, analysis, and tracking system may have a geometric constraint that may include a range of values associated with a length of a forearm of a typical human stored therein. Thus, according to an example embodiment, the target recognition, analysis, and tracking system may further adjust a dimension, a location or position, or the like of one or more body parts of the model based on one or more geometric constraints determined for the human target and/or associated with a typical human at 1030.

At 1035, a determination may be made regarding whether the model may be valid. For example, in one embodiment, the target recognition, analysis, and tracking system may further determine whether the model including the one or more body parts such as the joints j1-j16 of the model may be in a valid pose, whether the model may have conformed appropriately to values such as the X-values, Y-values, and/or depth values of the voxels associated with the human target, or the like. Thus, in one example embodiment, the target recognition, analysis, and tracking system may check the model, at 1035, where adjustments to the one or more body parts of the model, for example, at 1015, 1025, and/or 1030, may have caused the model to conform inappropriately to the voxels of the human target, to collapse in an invalid pose or an inappropriate manner, or the like. For example, the target recognition, analysis, and tracking system may check to determine whether a body part such as the joints j9 and j10, shown in FIG. 9, may be poking out such that the model may be stuck in an invalid pose. According to example embodiments, if the model may be invalid such as in invalid pose, not properly conformed to the voxels, or the like, the target recognition, analysis, and tracking system may adjust the one or more body parts of the model to previous locations or positions that may have been valid or in a valid pose and/or may have conformed properly to the voxels of the human target; may return or render an error message; may adjust the model based on values of the pixels associated with the human target in the depth image, or may perform any other suitable action.

At 1040, one or more body parts of the model may be refined. For example, in one embodiment, the target recognition, analysis, and tracking system may refine a location or a position of the one or more body parts such as the joints j1-j16 of the model. According to one embodiment, the target recognition, analysis, and tracking system may further refine a location or position of a body part of the model based on X-values, Y-values, and depth values in the 2-D pixel area of the non-downsampled depth image received at 305. For example, in one embodiment, the target recognition, analysis, and tracking system may use the data from the non-downsampled depth image to refine the location or position of the one or more body parts of the model where, for example, the model may collapse and/or where a location or position determined or estimated for one or more extremity of the human target in the grid of voxels may be inaccurate or invalid. Additionally, the target recognition, analysis, and tracking system may use the data from the now-downsampled depth image to refine the location or position of the joints of the model associated with frequently used gestures. For example, according to one embodiment, the target recognition, analysis, and tracking system may prioritize the joints associated with the hands. At 1040, the target recognition, analysis, and tracking system may localize the data around the hand in the non-downsampled depth image received at 305 such that the target recognition, analysis, and tracking system may modify the location or position of the body part of the model associated with the had using the higher resolution data in the non-downsampled depth image received at 305.

Referring back to FIG. 5, at 330, the model may be processed. For example, in one embodiment, the target recognition, analysis, and tracking system may process the model by, for example, mapping one or more motions or movements applied to the adjusted model to an avatar or game character such that the avatar or game character may be animated to mimic the user such as the user 18 described above with respect to FIGS. 1A and 1B. For example, the visual appearance of an on-screen character may be changed in response to changes to the model being adjusted.

In another embodiment, the target, recognition, analysis, and tracking system may process the model by providing the model to a gestures library in a computing environment such as the computing environment 12 described above with respect to FIGS. 1A-4. The gestures library may then be used to determine controls to perform within an application based on positions of various body parts of the model.

It should be understood that the configurations and/or approaches described herein are exemplary in nature, and that these specific embodiments or examples are not to be considered limiting. The specific routines or methods described herein may represent one or more of any number of processing strategies. As such, various acts illustrated may be performed in the sequence illustrated, in other sequences, in parallel, or the like. Likewise, the order of the above-described processes may be changed.

The subject matter of the present disclosure includes all novel and nonobvious combinations and subcombinations of the various processes, systems and configurations, and other features, functions, acts, and/or properties disclosed herein, as well as any and all equivalents thereof. 

What is claimed:
 1. A method for tracking a user, comprising: receiving a first depth image, the first depth image being captured by a depth camera; based at least on identifying an estimated location of an extremity of a user in the first depth image, positioning a part of a model to correspond to the estimated location of the extremity in the first depth image; and based at least on determining that a location of an extremity has not been estimated, adjusting a part of the model that is located close to a portion of the depth image to correspond to the portion of the depth image.
 2. The method of claim 1, wherein adjusting the part of the model that is located close to the portion of the depth image to correspond to the portion of the depth image comprises adjusting the part of the model to a location of at least one voxel in the first depth image.
 3. The method of claim 2, wherein adjusting the part of the model to a location of at least one voxel in the first depth image comprises adjusting the part of the model to a location defined by an x-value, y-value, and depth value in the first depth image.
 4. The method of claim 1, wherein adjusting the part of the model that is located close to a portion of the depth image to correspond to the portion of the depth image comprises adjusting the part of the model to correspond to a portion of the depth image that represents the user.
 5. The method of claim 1, further comprising: based least on determining that the location of the extremity has not been estimated, positioning the model over the first depth image, wherein adjusting the part of the model that is located close to the portion of the depth image to correspond to the portion of the depth image comprises adjusting the part of the model to a portion of the depth image that is located closest to the part of the model.
 6. The method of claim 1, wherein adjusting the part of the model that is located close to the portion of the depth image to correspond to the portion of the depth image comprises magnetizing the part of the model to a portion of the depth image.
 7. The method of claim 6, wherein magnetizing the part of the model to a portion of the depth image comprises magnetizing the part of the model to voxels defining a boundary or surface in the first depth image.
 8. The method of claim 1, wherein adjusting the part of the model that is located close to the portion of the depth image comprises adjusting the part of the model to correspond to voxels defining a boundary or surface in the first depth image.
 9. The method of claim 1, wherein adjusting the part of the model that is located close to the portion of the depth image to correspond to the portion of the depth image comprises adjusting a joint of a skeletal model.
 10. The method of claim 1, wherein identifying an estimated location of an extremity of a user in the first depth image includes determining the location of the extremity of the user.
 11. A system, comprising: at least one processor; and computing memory communicatively coupled to the at least one processor, the computing memory comprising executable instructions that upon execution cause the system to perform operations comprising: receiving a first depth image, the first depth image being captured by a depth camera; identifying an estimated location or position of a first extremity of the user in the first depth image; positioning a first part of a model to correspond to the estimated location or position of the first extremity in the first depth image; and based at least on determining that a location or position of a second extremity has not been estimated, adjusting a second part of the model to correspond to a portion of the depth image that is located close to the second part of the model.
 12. The system of claim 11, wherein adjusting a second part of the model to correspond to a portion of the depth image that is located close to the second part of the model comprises adjusting the second part of the model to a location of at least one voxel in the first depth image.
 13. The system of claim 12, wherein adjusting the second part of the model to a location of at least one voxel in the first depth image comprises adjusting the second part of the model to a location defined by an x-value, y-value, and depth value in the first depth image.
 14. The system of claim 12, wherein adjusting a second part of the model to correspond to a portion of the depth image that is located close to the second part of the model comprises adjusting the second part of the model to correspond to a portion of the depth image that represents the user.
 15. The system of claim 11, further comprising executable instructions that upon execution cause the system to perform operations comprising: based at least on determining that the location or the position of the second extremity has not been estimated, positioning the model over the first depth image, wherein adjusting a second part of the model to correspond to a portion of the depth image that is located close to the second part of the model comprises adjusting the second part of the model to a portion of the depth image that is located closest to the second part of the model.
 16. The system of claim 11, wherein adjusting a second part of the model to correspond to a portion of the depth image that is located close to the second part of the model comprises magnetizing the second part of the model to a portion of the depth image.
 17. The system of claim 16, wherein magnetizing the second part of the model to a portion of the depth image comprises magnetizing the second part of the model to voxels defining a boundary or surface in the first depth image.
 18. The system of claim 11, wherein adjusting a second part of the model to correspond to a portion of the depth image that is located close to the second part of the model comprises adjusting the second part of the model to correspond to voxels defining a boundary or surface in the first depth image.
 19. The system of claim 11, wherein a second part of the model to correspond to a portion of the depth image that is located close to the second part of the model comprises adjusting a joint of a skeletal model.
 20. The system of claim 11, further comprising executable instructions that upon execution cause the system to perform operations comprising: downsampling pixels in the depth image before identifying an estimated location or position of a first extremity of the user in the first depth image.
 21. The method of claim 11, wherein identifying an estimated location or position of a first extremity of the user in the first depth image comprises determining the location or position of the extremity of the user.
 22. A method for tracking a user, comprising: receiving a first depth image, the first depth image being captured by a depth camera; based at least on identifying an estimated location of an extremity of a user in the first depth image, positioning a part of a model to correspond to the estimated location of the extremity in the first depth image; and based at least on determining a failure to estimate a location of an extremity, adjusting a part of the model that is located close to a portion of the depth image to correspond to the portion of the depth image.
 23. The method of claim 22, wherein identifying an estimated location of an extremity of a user in the first depth image includes determining the location of the extremity of the user. 